Inverse of sum of Kronecker products as a sum of Kronecker products
Sign Up to like & getrecommendations! Published in 2018 at "GPS Solutions"
DOI: 10.1007/s10291-018-0789-8
Abstract: In the context of processing global navigation satellite system (GNSS) data by least squares adjustment, one may encounter a mathematical problem when inverting a sum of two Kronecker products. As a solution of this problem,… read more here.
Keywords: kronecker; matrix inversion; sum kronecker; kronecker products ... See more keywords
Implicit vs. Explicit Approximate Matrix Inversion for Wideband Massive MU-MIMO Data Detection
Sign Up to like & getrecommendations! Published in 2018 at "Journal of Signal Processing Systems"
DOI: 10.1007/s11265-017-1313-z
Abstract: Massive multi-user (MU) MIMO wireless technology promises improved spectral efficiency compared to that of traditional cellular systems. While data-detection algorithms that rely on linear equalization achieve near-optimal error-rate performance for massive MU-MIMO systems, they require… read more here.
Keywords: detection; data detection; implicit explicit; matrix inversion ... See more keywords
Unfolding quantum computer readout noise
Sign Up to like & getrecommendations! Published in 2019 at "npj Quantum Information"
DOI: 10.1038/s41534-020-00309-7
Abstract: In the current era of noisy intermediate-scale quantum computers, noisy qubits can result in biased results for early quantum algorithm applications. This is a significant challenge for interpreting results from quantum computer simulations for quantum… read more here.
Keywords: quantum computer; matrix inversion; physics; readout errors ... See more keywords
Enabling Privacy-Preserving Parallel Outsourcing Matrix Inversion in IoT
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Internet of Things Journal"
DOI: 10.1109/jiot.2022.3150956
Abstract: With the rapid development of Internet of Things (IoT), edge computing has been widely applied as a novel computing paradigm. Securely outsourcing intensive tasks to edge servers is becoming increasingly pervasive. It is a nice… read more here.
Keywords: matrix inversion; inversion; edge servers; privacy preserving ... See more keywords
Revisiting the Adjoint Matrix for FPGA Calculating the Triangular Matrix Inversion
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Transactions on Circuits and Systems II: Express Briefs"
DOI: 10.1109/tcsii.2020.3034437
Abstract: Due to the robustness, the matrix inversion methods based on matrix factorization are often adopted in engineering while the triangular matrix inversion is one key part of those methods. This brief reviews the adjoint matrix… read more here.
Keywords: adjoint matrix; matrix inversion; matrix; calculating triangular ... See more keywords
A Robust Predefined-Time Convergence Zeroing Neural Network for Dynamic Matrix Inversion
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Cybernetics"
DOI: 10.1109/tcyb.2022.3179312
Abstract: As a classical and effective method for solving various time-varying problems, the zeroing neural network (ZNN) is widely applied in the scientific and industrial realms. In plentiful studies on the ZNN model, its robustness and… read more here.
Keywords: time; matrix inversion; dynamic matrix; convergence ... See more keywords
GNN Model for Time-Varying Matrix Inversion With Robust Finite-Time Convergence.
Sign Up to like & getrecommendations! Published in 2022 at "IEEE transactions on neural networks and learning systems"
DOI: 10.1109/tnnls.2022.3175899
Abstract: As a type of recurrent neural networks (RNNs) modeled as dynamic systems, the gradient neural network (GNN) is recognized as an effective method for static matrix inversion with exponential convergence. However, when it comes to… read more here.
Keywords: time; matrix inversion; varying matrix; inversion ... See more keywords
Design and Analysis of a Novel Finite-Time Convergent and Noise-Tolerant Recurrent Neural Network for Time-Variant Matrix Inversion
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Transactions on Systems, Man, and Cybernetics: Systems"
DOI: 10.1109/tsmc.2018.2853598
Abstract: Matrix inversion ubiquitously arises in engineering. The so-called zeroing neural network (ZNN) is an effective recurrent neural network for solving time-variant matrix inversion. Without considering noises, the ZNN approach requires finite time or infinitely long… read more here.
Keywords: finite time; neural network; time; matrix inversion ... See more keywords
Design of a nonlinearly activated gradient-based neural network and its application to matrix inversion
Sign Up to like & getrecommendations! Published in 2020 at "Filomat"
DOI: 10.2298/fil2015095z
Abstract: Different from the traditional linearly activated gradient-based neural network model (GNN model), two nonlinear activation functions are presented and investigated to construct two nonlinear gradient-based neural network models (NGNN-1 model and NGNN-2 model) for matrix… read more here.
Keywords: neural network; matrix inversion; gradient based; based neural ... See more keywords
An Efficient Precoding Algorithm for mmWave Massive MIMO Systems
Sign Up to like & getrecommendations! Published in 2019 at "Symmetry"
DOI: 10.3390/sym11091099
Abstract: Symmetrical precoding and algorithms play a vital role in the field of wireless communications and cellular networks. This paper proposed a low-complexity hybrid precoding algorithm for mmWave massive multiple-input multiple-output (MIMO) systems. The traditional orthogonal… read more here.
Keywords: algorithm mmwave; algorithm; matrix inversion; precoding algorithm ... See more keywords